Statistical and spatial analyses were conducted according to the methods described in Figure 4 . Software included SPSS Statistic 26, SPSS AMOS 25, QGIS 3.18.2, and ArcGIS Pro.
Spss statistic 26
Manufactured by IBM
Sourced in United States
SPSS Statistics 26 is a software application developed by IBM for statistical analysis. It provides a comprehensive set of tools for data management, analysis, and reporting. The core function of SPSS Statistics 26 is to enable users to explore, analyze, and interpret data to uncover insights and support decision-making.
Automatically generated - may contain errors
Lab products found in correlation
Spss amos 25, by IBM (1 mentions)
Image studio digits v 5, by LI COR (1 mentions)
Matlab, by MathWorks (1 mentions)
Prism 9, by GraphPad (1 mentions)
Design expert 13, by Stat-Ease (1 mentions)
Prism 8, by IBM (1 mentions)
Excel 2010, by IBM (1 mentions)
Origin 9, by OriginLab (1 mentions)
Amos 22, by IBM (1 mentions)
Prism 8, by GraphPad (1 mentions)
Spss statistics 26, by IBM (1 mentions)
50 protocols using spss statistic 26
1
Spatial and Statistical Analysis Methods
2
Comparing Measurement Techniques with ANOVA
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With the purpose of determining the significant differences between measurements, analysis of variance (ANOVA) was carried out by means of SPSS software (SPSS Statistic 26). Tukey’s multiple range test was used for multiple comparisons among different systems with a statistical significance at the p < 0.05 level.
3
Evaluating CES Well-being Factors
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Correlations between CES-associated well-being scores and features of interest were performed through standard multiple regression modelling using SPSS Statistic 26 [39 ]. Correlations determined if there were influences from the features of interest on respondent valuation over their CES-related well-being related. The dependent variable was the factor scores for each dimension of CES well-being, and the independent variables were the distance to and visibility of features of interest. Since seven dimensions of CES well-being were adapted [4 (link)], the models were run seven times, corresponding to each factor.
4
Statistical Analysis of Quantitative Data
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All quantitative data are represented as the mean ± standard deviation (SD) of three or more independent experiments. When the number of experimental groups was 2, the results were analyzed using a t-test. All of these analytical methods were performed using SPSS statistic 26 (SPSS Inc., Chicago, IL, USA). p < 0.05 was considered significant.
5
Antimicrobial Activity of Actinomycetia Strains
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All experiments were performed in biological triplicate. The data were stated as the mean ± standard error of the mean. One-way ANOVA was performed in SPSS statistic 26.0 for the variation of inhibition among different actinomycetia strains with the 3 groups (3 MDR pathogens). A p < 0.001 was measured as significant. The test for variances within the strains in the antimicrobial assay is adjusted for all pairwise comparisons using the Bonferroni correction.
6
Attachment and Psychopathology in Counseling Attendance
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All analyses were conducted using the software SPSS Statistic 26.0. Pearson’s
chi-squared test has been used to compare counselling attending students and
counselling non-attending students in relation to the distribution of attachment
and psychopathological range. MANCOVAs have been used to compare the two groups
with respect to the SCL-90 R scales and the ASQ scales. Correlation analyses
have been carried out in each group to identify associations between the
examined variables.
chi-squared test has been used to compare counselling attending students and
counselling non-attending students in relation to the distribution of attachment
and psychopathological range. MANCOVAs have been used to compare the two groups
with respect to the SCL-90 R scales and the ASQ scales. Correlation analyses
have been carried out in each group to identify associations between the
examined variables.
7
Sociodemographic Factors and Mental Health
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Descriptive statistics were calculated for sociodemographic characteristics and the main study outcomes (DASS-21, impact of self-event, and self-esteem). Percentages of responses were calculated according to the number of respondents per response, with respect to the number of total responses to a question. The scores from the IES-R, self-esteem, and DASS-21 subscales were expressed as mean and standard deviation. Moreover, independent sample t-test and one-way analysis of variable test were used to calculate the means of the main statistical outcomes according to sociodemographic data. Furthermore, the pearson correlation tests were used to examine the relationship between the sociodemographic data and the main study outcomes. Additionally, linear regressions tests were employed to calculate the univariate associations between sociodemographic characteristics and the study outcomes. All tests were two-tailed, with a significance level of p < 0.05. Statistical analysis was performed using SPSS Statistic 26.0.
8
Assessing Participant Characteristics and Scale Reliability
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Characteristics of participants are described by absolute and relative frequencies and compared through the Chi-square test or Fisher's exact test, as appropriate, in categorical variables. Medians and percentiles, 25 and 75 (P25-P75), were used to describe continuous variables compared using the Mann-Whitney test. Also, we assessed the internal consistency of the HADS scale using Cronbach's alpha.
Data analysis was performed using the SPSS Statistic 26.0 (IBM SPSS, New York, USA). A significance level of 0.05 was used.
Data analysis was performed using the SPSS Statistic 26.0 (IBM SPSS, New York, USA). A significance level of 0.05 was used.
9
Statistical Analysis of Participant Characteristics
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Characteristics of participants are described by absolute and relative frequencies and compared through the Chi-square test or Fisher's exact test, as appropriate, in categorical variables.
Medians and standard deviations (SD) were used to describe continuous variables compared using the Mann-Whitney test. Data analysis was performed using the SPSS Statistic 26.0 (IBM SPSS, New York, USA). A significance level of 0.05 was used.
Medians and standard deviations (SD) were used to describe continuous variables compared using the Mann-Whitney test. Data analysis was performed using the SPSS Statistic 26.0 (IBM SPSS, New York, USA). A significance level of 0.05 was used.
10
Plasma Stress and Immune Analysis
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The plasma stress and immune data were analyzed with independent sample T tests using IBM SPSS Statistic 26 software (IBM Corp., Armonk, NY). The data are presented as the means ± SE. Significant differences were reported as those with p < 0.05.
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